from abc import ABCMeta, abstractmethod from typing import Any, Optional, Callable, Tuple from enum import Enum, auto import numpy as np from apt.utils.datasets import Dataset, Data, OUTPUT_DATA_ARRAY_TYPE from art.estimators.classification import BlackBoxClassifier from art.utils import check_and_transform_label_format def is_one_hot(y: OUTPUT_DATA_ARRAY_TYPE) -> bool: return len(y.shape) == 2 and y.shape[1] > 1 def get_nb_classes(y: OUTPUT_DATA_ARRAY_TYPE) -> int: """ Get the number of classes from an array of labels :param y: the labels :type y: numpy array :return: the number of classes as integer """ if y is None: return 0 if type(y) != np.ndarray: raise ValueError("Input should be numpy array") if is_one_hot(y): return y.shape[1] else: return int(np.max(y) + 1) class ModelOutputType(Enum): CLASSIFIER_PROBABILITIES = auto() # vector of probabilities CLASSIFIER_LOGITS = auto() # vector of logits CLASSIFIER_SCALAR = auto() # label only REGRESSOR_SCALAR = auto() # value class ScoringMethod(Enum): ACCURACY = auto() # number of correct predictions divided by the number of samples MEAN_SQUARED_ERROR = auto() # mean squared error between the predictions and true labels class Model(metaclass=ABCMeta): """ Abstract base class for ML model wrappers. :param model: The original model object (of the underlying ML framework) :type model: framework-specific model object :param output_type: The type of output the model yields (vector/label only for classifiers, value for regressors) :type output_type: `ModelOutputType` :param black_box_access: Boolean describing the type of deployment of the model (when in production). Set to True if the model is only available via query (API) access, i.e., only the outputs of the model are exposed, and False if the model internals are also available. Default is True. :type black_box_access: boolean, optional :param unlimited_queries: If black_box_access is True, this boolean indicates whether a user can perform unlimited queries to the model API or whether there is a limit to the number of queries that can be submitted. Default is True. :type unlimited_queries: boolean, optional """ def __init__(self, model: Any, output_type: ModelOutputType, black_box_access: Optional[bool] = True, unlimited_queries: Optional[bool] = True, **kwargs): self._model = model self._output_type = output_type self._black_box_access = black_box_access self._unlimited_queries = unlimited_queries @abstractmethod def fit(self, train_data: Dataset, **kwargs) -> None: """ Fit the model using the training data. :param train_data: Training data. :type train_data: `Dataset` """ raise NotImplementedError @abstractmethod def predict(self, x: Dataset, **kwargs) -> OUTPUT_DATA_ARRAY_TYPE: """ Perform predictions using the model for input `x`. :param x: Input samples. :type x: `Dataset` :return: Predictions from the model as numpy array. """ raise NotImplementedError @abstractmethod def score(self, test_data: Dataset, **kwargs): """ Score the model using test data. :param test_data: Test data. :type train_data: `Dataset` :return: the score as float (for classifiers, between 0 and 1) """ return NotImplementedError @property def model(self) -> Any: """ Return the underlying model. :return: The model. """ return self._model @property def output_type(self) -> ModelOutputType: """ Return the model's output type. :return: The model's output type. """ return self._output_type @property def black_box_access(self) -> bool: """ Return whether the model is only available via query (API) access, i.e., only the outputs of the model are exposed, or if the model internals are also available. :return: True if the model is only available via query (API) access, otherwise False. """ return self._black_box_access @property def unlimited_queries(self) -> bool: """ If black_box_access is True, return whether a user can perform unlimited queries to the model API or whether there is a limit to the number of queries that can be submitted. :return: True if a user can perform unlimited queries to the model API, otherwise False. """ return self._unlimited_queries class BlackboxClassifier(Model): """ Wrapper for black-box ML classification models. :param model: The training and/or test data along with the model's predictions for the data or a callable predict method. :type model: `Data` object or Callable :param output_type: The type of output the model yields (vector/label only) :type output_type: `ModelOutputType` :param black_box_access: Boolean describing the type of deployment of the model (when in production). Always assumed to be True (black box) for this wrapper. :type black_box_access: boolean, optional :param unlimited_queries: Boolean indicating whether a user can perform unlimited queries to the model API. :type unlimited_queries: boolean, optional """ def __init__(self, model: Any, output_type: ModelOutputType, black_box_access: Optional[bool] = True, unlimited_queries: Optional[bool] = True, **kwargs): super().__init__(model, output_type, black_box_access=True, unlimited_queries=unlimited_queries, **kwargs) self._nb_classes = None self._input_shape = None @property def nb_classes(self) -> int: """ Return the number of prediction classes of the model. :return: Number of prediction classes of the model. """ return self._nb_classes @property def input_shape(self) -> Tuple[int, ...]: """ Return the shape of input to the model. :return: Shape of input to the model. """ return self._input_shape def fit(self, train_data: Dataset, **kwargs) -> None: """ A blackbox model cannot be fit. """ raise NotImplementedError def predict(self, x: Dataset, **kwargs) -> OUTPUT_DATA_ARRAY_TYPE: """ Get predictions from the model for input `x`. `x` must be a subset of the data provided in the `model` data in `__init__()`. :param x: Input samples. :type x: `Dataset` :return: Predictions from the model as numpy array. """ return self._art_model.predict(x.get_samples()) def score(self, test_data: Dataset, scoring_method: Optional[ScoringMethod] = ScoringMethod.ACCURACY, **kwargs): """ Score the model using test data. :param test_data: Test data. :type train_data: `Dataset` :param scoring_method: The method for scoring predictions. Default is ACCURACY. :type scoring_method: `ScoringMethod`, optional :return: the score as float (for classifiers, between 0 and 1) """ predicted = self._art_model.predict(test_data.get_samples()) y = check_and_transform_label_format(test_data.get_labels(), nb_classes=self._nb_classes) if scoring_method == ScoringMethod.ACCURACY: return np.count_nonzero(np.argmax(y, axis=1) == np.argmax(predicted, axis=1)) / predicted.shape[0] else: raise NotImplementedError class BlackboxClassifierPredictions(BlackboxClassifier): """ Wrapper for black-box ML classification models using data and predictions. :param model: The training and/or test data along with the model's predictions for the data. Assumes that the data is represented as numpy arrays. Labels are expected to either be class probabilities (multi-column) or a 1D-array of categorical labels (consecutive integers starting at 0). :type model: `Data` object :param output_type: The type of output the model yields (vector/label only) :type output_type: `ModelOutputType` :param black_box_access: Boolean describing the type of deployment of the model (when in production). Always assumed to be True for this wrapper. :type black_box_access: boolean, optional :param unlimited_queries: Boolean indicating whether a user can perform unlimited queries to the model API. Always assumed to be False for this wrapper. :type unlimited_queries: boolean, optional """ def __init__(self, model: Data, output_type: ModelOutputType, black_box_access: Optional[bool] = True, unlimited_queries: Optional[bool] = True, **kwargs): super().__init__(model, output_type, black_box_access=True, unlimited_queries=False, **kwargs) x_train_pred = model.get_train_samples() y_train_pred = model.get_train_labels() x_test_pred = model.get_test_samples() y_test_pred = model.get_test_labels() if y_train_pred is not None and len(y_train_pred.shape) == 1: self._nb_classes = get_nb_classes(y_train_pred) y_train_pred = check_and_transform_label_format(y_train_pred, nb_classes=self._nb_classes) if y_test_pred is not None and len(y_test_pred.shape) == 1: if self._nb_classes is None: self._nb_classes = get_nb_classes(y_test_pred) y_test_pred = check_and_transform_label_format(y_test_pred, nb_classes=self._nb_classes) if x_train_pred is not None and y_train_pred is not None and x_test_pred is not None and y_test_pred is not None: if type(y_train_pred) != np.ndarray or type(y_test_pred) != np.ndarray \ or type(y_train_pred) != np.ndarray or type(y_test_pred) != np.ndarray: raise NotImplementedError("X/Y Data should be numpy array") x_pred = np.vstack((x_train_pred, x_test_pred)) y_pred = np.vstack((y_train_pred, y_test_pred)) elif x_test_pred is not None and y_test_pred is not None: x_pred = x_test_pred y_pred = y_test_pred elif x_train_pred is not None and y_train_pred is not None: x_pred = x_train_pred y_pred = y_train_pred else: raise NotImplementedError("Invalid data - None") self._nb_classes = get_nb_classes(y_pred) self._input_shape = x_pred.shape[1:] predict_fn = (x_pred, y_pred) self._art_model = BlackBoxClassifier(predict_fn, self._input_shape, self._nb_classes, fuzzy_float_compare=True) class BlackboxClassifierPredictFunction(BlackboxClassifier): """ Wrapper for black-box ML classification models using a predict function. :param model: Function that takes in an `np.ndarray` of input data and returns predictions either as class probabilities (multi-column) or a 1D-array of categorical labels (consecutive integers starting at 0). :type model: Callable :param output_type: The type of output the model yields (vector/label only) :type output_type: `ModelOutputType` :param input_shape: Shape of input to the model. :type input_shape: Tuple[int, ...] :param nb_classes: Number of prediction classes of the model. :type nb_classes: int :param black_box_access: Boolean describing the type of deployment of the model (when in production). Always assumed to be True for this wrapper. :type black_box_access: boolean, optional :param unlimited_queries: Boolean indicating whether a user can perform unlimited queries to the model API. :type unlimited_queries: boolean, optional """ def __init__(self, model: Callable, output_type: ModelOutputType, input_shape: Tuple[int, ...], nb_classes: int, black_box_access: Optional[bool] = True, unlimited_queries: Optional[bool] = True, **kwargs): super().__init__(model, output_type, black_box_access=True, unlimited_queries=unlimited_queries, **kwargs) self._nb_classes = nb_classes self._input_shape = input_shape self._art_model = BlackBoxClassifier(model, self._input_shape, self._nb_classes)